Alternativas para la medición partimos de una idea elemental: exportaciones por trabajador como proxy de la productividad ES posible que ahí las magnitudes jueguen un rol que rompa cosas por lo que mejor ponerlo en términos relativos
$\dfrac{X_{HS;s;p}}{L_{s,p}$
dos pistas que nos pueden ayudar a pensar:
$VCR = \dfrac{\dfrac{X_{HS;p}} {X_{HS;w}}} {\dfrac{X_p}{X_w}}$
$LCR = \dfrac{\dfrac{L_{s;p}}{L_{s;w}}} {\dfrac{L_p}{L_w}}$
capital/trabajo intensivo:
$intensidad = \dfrac{\dfrac{VA_{s;p}}{VA_p}} {\dfrac{L_{s;p}}{L_p}}$
$\dfrac{\dfrac{X_{HS;s;p}}{X_p}} {\dfrac{L_{s,p}}{L_p}}$
$\dfrac{\dfrac{X_{HS;s;p}}{X_{HS;s;w}}} {\dfrac{L_{s,p}}{L_{s,w}}}$
opción 3
$\dfrac{VCR}{LCR}$
| t | country | k | v | expos_totales_pais | expos_mundo_producto | expos_totales_mundo | prop_expo | VCR | VCR_norm | VCR_existe | description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2002 | af | 80232 | 512.835 | 45183.569 | 302843.310 | 6.404218e+09 | 0.011350 | 240.018790 | 0.991702 | 1.0 | Nuts, edible: walnuts, fresh or dried, shelled |
| 2 | 2002 | af | 12111 | 819.028 | 45183.569 | 29169.439 | 6.404218e+09 | 0.018127 | 3979.754121 | 0.999498 | 1.0 | Liquorice roots used primarily in perfumery, i... |
| 3 | 2002 | af | 12119 | 80.016 | 45183.569 | 880996.538 | 6.404218e+09 | 0.001771 | 12.873250 | 0.855838 | 1.0 | Plants and parts (including seeds and fruits) ... |
| 4 | 2002 | af | 130212 | 1.155 | 45183.569 | 84206.964 | 6.404218e+09 | 0.000026 | 1.944104 | 0.320676 | 1.0 | Vegetable saps and extracts: of liquorice |
| 6 | 2002 | af | 540769 | 15.181 | 45183.569 | 848628.830 | 6.404218e+09 | 0.000336 | 2.535527 | 0.434313 | 1.0 | Fabrics, woven: containing less than 85% by we... |
| v | prop_hs_pais | prop_hs_mundo | prop_pais_mundo | |
|---|---|---|---|---|
| v | 1.000000 | 0.217609 | 0.430533 | -0.007933 |
| prop_hs_pais | 0.217609 | 1.000000 | 0.075663 | 0.212244 |
| prop_hs_mundo | 0.430533 | 0.075663 | 1.000000 | -0.016211 |
| prop_pais_mundo | -0.007933 | 0.212244 | -0.016211 | 1.000000 |
############################### 0 ######################### ############################### 1 ######################### ############################### 2 ######################### ############################### 3 ######################### ############################### 4 ######################### ############################### 5 ######################### ############################### 6 ######################### ############################### 7 ######################### ############################### 8 ######################### ############################### 9 #########################
| runs | parametros | kl_score | best | |
|---|---|---|---|---|
| 0 | 0 | {'perplexity': 15, 'metric': 'cosine'} | 0.290841 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 1 | 0 | {'perplexity': 70, 'metric': 'correlation'} | 0.594407 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 2 | 0 | {'perplexity': 70, 'metric': 'cosine'} | 0.594407 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 3 | 0 | {'perplexity': 55, 'metric': 'cosine'} | 0.594407 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 4 | 0 | {'perplexity': 60, 'metric': 'correlation'} | 0.594407 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| ... | ... | ... | ... | ... |
| 95 | 9 | {'perplexity': 20, 'metric': 'cosine'} | 0.254610 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 96 | 9 | {'perplexity': 20, 'metric': 'correlation'} | 0.330467 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 97 | 9 | {'perplexity': 25, 'metric': 'correlation'} | 0.315320 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 98 | 9 | {'perplexity': 60, 'metric': 'cosine'} | 0.595184 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 99 | 9 | {'perplexity': 35, 'metric': 'cosine'} | 0.431405 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
100 rows × 4 columns
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| runs | parametros | kl_score | best | |
|---|---|---|---|---|
| 0 | 0 | {'perplexity': 15, 'metric': 'cosine'} | 0.290841 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 1 | 0 | {'perplexity': 70, 'metric': 'correlation'} | 0.594407 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 2 | 0 | {'perplexity': 70, 'metric': 'cosine'} | 0.594407 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 3 | 0 | {'perplexity': 55, 'metric': 'cosine'} | 0.594407 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 4 | 0 | {'perplexity': 60, 'metric': 'correlation'} | 0.594407 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| ... | ... | ... | ... | ... |
| 95 | 9 | {'perplexity': 20, 'metric': 'cosine'} | 0.254610 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 96 | 9 | {'perplexity': 20, 'metric': 'correlation'} | 0.330467 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 97 | 9 | {'perplexity': 25, 'metric': 'correlation'} | 0.315320 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 98 | 9 | {'perplexity': 60, 'metric': 'cosine'} | 0.595184 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
| 99 | 9 | {'perplexity': 35, 'metric': 'cosine'} | 0.431405 | TSNE(metric='cosine', n_iter=5000, perplexity=... |
100 rows × 4 columns
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[77], line 3 1 new_sector1 = new_sector.merge(empleo[['country', 'time', 'cod_numeric', 'empleo' ]], left_on=['country', 't' , 'CIIU_2d'], right_on=['country', 'time', 'cod_numeric'], how= 'left') 2 new_sector1['productividad'] = new_sector1.v/new_sector1.empleo ----> 3 plot_clusters_productivity(new_sector1, 'productividad') TypeError: plot_clusters_productivity() missing 1 required positional argument: 'df_cluster'